Here is a useful set of Decision Management Assets on Modernizing Operational Decisions from SAS Software with Decision Management Solutions’ CEO James Taylor. The set includes two decision management videos – one aimed at business users and one at IT professionals: For Business Users the first video is an interview between Fiona McNeil of SAS and Jame […]

SAS® Model Manager is getting an update soon to release 13.1 (I last blogged about Model Manager 3.1). The vision of SAS Model Manager going forward is to streamline the integration of predictive modeling into the overall environment, make it easier to operationalize analytical models, expand the model portfolio management capabilities and improve governance and monitoring of large numbers of models.

The new release will be standardized on the web-based application framework, making SAS Model Manager 100% browser-based – importing models, setting up champion-challenger, viewing performance etc. This web interface also makes these capabilities easier to access from within the SAS Decision Manager environment (reviewed here which includes the SAS Model Manager functionality).

SAS® Enterprise Miner recently got a major release – 13.1 – focused on machine learning, scalability and productivity. It’s been a while since I blogged about SAS Enterprise Miner (last review here) so this might not be a complete list of the improvements since then.

The machine learning focus added High Performance Support Vector Machines and Clustering while upgrading the HP Neural Network and Random Forest algorithms. From a scalability perspective the Principal Components, GLM, Bayeisan and Time Series Data Mining algorithms were all updated for high performance (multi-threaded parallelism etc). The high performance algorithms work the same and are dropped into SAS Enterprise Miner as nodes the same as always. These high performance data mining algorithms have been added in each of the last few versions. The intent of all this work is to support modelers so they can use all the data th

SAS is upgrading its in-memory analytics products with SAS® Visual Statistics (forthcoming) and SAS® In-Memory Statistics for Hadoop. SAS In-Memory Statistics for Hadoop is available now and SAS Visual Statistics is going to be shipping in July of 2014.

SAS Visual Statistics is based on the SAS® LASR™ Analytic Server for in-memory processing and is aimed at both data exploration/discovery and predictive modeling. It will include SAS® Visual Analytics and likewise offers an interactive, drag and drop 100% web-based interface. It will run in single machine mode in distributed environments (Teradata, Greenplum and Hadoop, both Cloudera and Hortonworks) for scale.

I am speaking at the Bay Area SAS User Group October 15 at 3pm on Improving Analytic Results with Decision Modeling Established analytic approaches like CRISP-DM stress the importance of understanding the project objectives and requirements from a business perspective, but to date there are few formal approaches to capturing this understanding in a repeatable, understandable […]

SAS has today announced its new Decision Management product – SAS® Decision Manager . Over recent years SAS sees an increasing focus from clients on using analytics both for strategic decision-making and operational decision-making. Organizations seeking to develop analytic solutions for operational decision-making have historically struggled as there is a high degree of dysfunction resulting […]

As part of our series on Marketing Decision Management Solutions, I got an update on SAS Real-time Decision Manager. SAS Real-time Decision Manager is a complete solution that delivers decision capabilities around customer intelligence for marketing and I was last updated on it back in 2010. The solution provides a unified capability for making customer […]

Paul Kent, VP of Big Data, came up next and gave a white board talk with no slides. His initial focus was on parallel processing for analytics, something that SAS has been working on for a while and that is key to their HPA and Big Data approaches. He points out that some problems (min […]